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1.
A key point in the application of multi‐model Bayesian averaging techniques to assess the predictive uncertainty in groundwater modelling applications is the definition of prior model probabilities, which reflect the prior perception about the plausibility of alternative models. In this work the influence of prior knowledge and prior model probabilities on posterior model probabilities, multi‐model predictions, and conceptual model uncertainty estimations is analysed. The sensitivity to prior model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior model probabilities. Additionally, the value of prior knowledge about alternative models in reducing conceptual model uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative models. A constrained maximum entropy approach is used to find the set of prior model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three‐dimensional hypothetical setup approximated by seven alternative conceptual models is employed. Results show that posterior model probabilities, leading moments of the predictive distributions and estimations of conceptual model uncertainty are very sensitive to prior model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi‐model approach, expressed by reductions of the multi‐model prediction variances by up to 60% compared with a non‐informative case. However, the ratio between‐model to total variance does not substantially decrease. This suggests that the contribution of conceptual model uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual model uncertainty in groundwater modelling predictions. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
2.
The integrated effects of climate and hydrologic uncertainty on future flood risk assessments 总被引：2，自引：0，他引：2 
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下载免费PDF全文 This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
3.
A methodology to derive solute transport models at any flow rate is presented. The novelty of the proposed approach lies in the assessment of uncertainty of predictions that incorporate parameterisation based on flow rate. A simple treatment of uncertainty takes into account heteroscedastic modelling errors related to tracer experiments performed over a range of flow rates, as well as the uncertainty of the observed flow rates themselves. The proposed approach is illustrated using two models for the transport of a conservative solute: a physically based, deterministic, advection-dispersion model (ADE), and a stochastic, transfer function based, active mixing volume model (AMV). For both models the uncertainty of any parameter increases with increasing flow rate (reflecting the heteroscedastic treatment of modelling errors at different observed flow rates), but in contrast the uncertainty of travel time, computed from the predicted model parameters, was found to decrease with increasing flow rate. 相似文献
4.
Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion. 相似文献
5.
Jun Guo Jianzhong Zhou Lixiang Song Qiang Zou Xiaofan Zeng 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(4):985-1004
Assessment of parameter and predictive uncertainty of hydrologic models is an essential part in the field of hydrology. However, during the past decades, research related to hydrologic model uncertainty is mostly done with conceptual models. As is accepted that uncertainty in model predictions arises from measurement errors associated with the system input and output, from model structural errors and from problems with parameter estimation. Unfortunately, non-conceptual models, such as black-box models, also suffer from these problems. In this paper, we take the artificial neural network (ANN) rainfall-runoff model as an example, and the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) is employed to analysis the parameter and predictive uncertainty of this model. Furthermore, based on the results of uncertainty assessment, we finally arrive at a simpler incomplete-connection artificial neural network (ICANN) model as well as with better performance compared to original ANN rainfall-runoff model. These results not only indicate that SCEM-UA can be a useful tool for uncertainty analysis of ANN model, but also prove that uncertainty does exist in ANN rainfall-runoff model. Additionally, in some way, it presents that the ICANN model is with smaller uncertainty than the original ANN model. 相似文献
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7.
Abstract The uncertainty associated with a rainfall–runoff and non-point source loading (NPS) model can be attributed to both the parameterization and model structure. An interesting implication of the areal nature of NPS models is the direct relationship between model structure (i.e. sub-watershed size) and sample size for the parameterization of spatial data. The approach of this research is to find structural limitations in scale for the use of the conceptual NPS model, then examine the scales at which suitable stochastic depictions of key parameter sets can be generated. The overlapping regions are optimal (and possibly the only suitable regions) for conducting meaningful stochastic analysis with a given NPS model. Previous work has sought to find optimal scales for deterministic analysis (where, in fact, calibration can be adjusted to compensate for sub-optimal scale selection); however, analysis of stochastic suitability and uncertainty associated with both the conceptual model and the parameter set, as presented here, is novel; as is the strategy of delineating a watershed based on the uncertainty distribution. The results of this paper demonstrate a narrow range of acceptable model structure for stochastic analysis in the chosen NPS model. In the case examined, the uncertainties associated with parameterization and parameter sensitivity are shown to be outweighed in significance by those resulting from structural and conceptual decisions. Citation Parker, G. T. Rennie, C. D. & Droste, R. L. (2011) Model structure and uncertainty for stochastic non-point source modelling applications. Hydrol. Sci. J. 56(5), 870–882. 相似文献
8.
Derek Karssenberg 《水文研究》2002,16(14):2751-2766
An evaluation is made of the suitability of programming languages for hydrological modellers to create distributed, process‐based hydrological models. Both system programming languages and high‐level environmental modelling languages are evaluated based on a list of requirements for the optimal programming language for such models. This is illustrated with a case study, implemented using the PCRaster environmental modelling language to create a distributed, process‐based hydrological model based on the concepts of KINEROS‐EUROSEM. The main conclusion is that system programming languages are not ideal for hydrologists who are not computer programmers because the level of thinking of these languages is too strongly related to specialized computer science. A higher level environmental modelling language is better in the sense that it operates at the conceptual level of the hydrologist. This is because it contains operators that identify hydrological processes that operate on hydrological entities, such as two‐dimensional maps, three‐dimensional blocks and time‐series. The case study illustrates the advantages of using an environmental modelling language as compared with system programming languages in fulfilling requirements on the level of thinking applied in the language, the reusability of the program code, the lack of technical details in the program, a short model development time and learnability. The study shows that environmental modelling languages are equally good as system programming languages in minimizing programming errors, but are worse in generic application and performance. It is expected that environmental modelling languages will be used in future mainly for development of new models that can be tailored to modelling aims and the field data available. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
9.
A review of advances in flash flood forecasting 总被引：1，自引：0，他引：1
Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damage to infrastructure and the environment. Research into the use of new modelling techniques and data types in flash flood forecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged from this research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotely sensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Over the past decade flash flood forecast lead‐time has expanded up to six hours due to improved rainfall forecasts. However the largest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physically based hydrological models have been developed and used for flash flood forecasting and they have been found to give more plausible results when compared with the results of conceptual, statistical, and neural network models. Among the three methods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance) is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existing model is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash flooding is increasing due to increasing urbanisation. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
10.
The use of detailed groundwater models to simulate complex environmental processes can be hampered by (1) long run‐times and (2) a penchant for solution convergence problems. Collectively, these can undermine the ability of a modeler to reduce and quantify predictive uncertainty, and therefore limit the use of such detailed models in the decision‐making context. We explain and demonstrate a novel approach to calibration and the exploration of posterior predictive uncertainty, of a complex model, that can overcome these problems in many modelling contexts. The methodology relies on conjunctive use of a simplified surrogate version of the complex model in combination with the complex model itself. The methodology employs gradient‐based subspace analysis and is thus readily adapted for use in highly parameterized contexts. In its most basic form, one or more surrogate models are used for calculation of the partial derivatives that collectively comprise the Jacobian matrix. Meanwhile, testing of parameter upgrades and the making of predictions is done by the original complex model. The methodology is demonstrated using a density‐dependent seawater intrusion model in which the model domain is characterized by a heterogeneous distribution of hydraulic conductivity. 相似文献
11.
In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box–Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box–Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box–Cox transformation is not applied or non-calibrated parameter is used. 相似文献
12.
Despite the abundance of existing hydrological models, there is no single model that has been identified as performing consistently over the range of possible catchment types and catchment conditions. An attractive alternative to selecting a single model is to combine the results from several different hydrological models, thereby providing a more appropriate representation of model uncertainty than is the case otherwise. Methods based on Bayesian statistical techniques provide an ideal means to compare and combine competing models, as they explicitly account for model uncertainty. Bayesian model averaging is one such alternative that combines individual models by weighting models proportional to their respective posterior probability of selection. However, the necessity of having fixed weights for each model over the entire length of the simulation period means that the relative usefulness of different models at different times is not considered. The hierarchical mixtures of experts (HME) framework is an appealing extension of the model averaging framework that allows the individual model weights to be estimated dynamically. Consequently, a model more capable at simulating low flow characteristics attains a higher weight (probability) when such conditions are likely, switching over to a lower weight when catchment storage increases. In this way, different models apply in different hydrological states, with the probability of selecting each model being allowed to depend on relevant antecedent condition characteristics. HME models provide additional flexibility compared with simple combinations of models, by allowing the way that model predictions are combined to depend on predictor variables. Thus, for hydrological models, the ‘switch’ from one model to another can depend on the existing catchment condition. This new modelling framework is applied using a simple conceptual model to 10 selected Australian catchments. The study regions are chosen to vary considerably in terms of size, yield and location. Results from this application are compared with the alternative where a single fixed model structure is applied. Comparison of the model simulations using the maximum log‐likelihood and the Nash‐Sutcliffe coefficient of efficiency show that more variance in streamflow was explained by the HME model, compared with the conceptual model alone for each of the catchments investigated. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
13.
The current state of knowledge regarding uncertainties in urban drainage models is poor. This is in part due to the lack of clarity in the way model uncertainty analyses are conducted and how the results are presented and used. There is a need for a common terminology and a conceptual framework for describing and estimating uncertainties in urban drainage models. Practical tools for the assessment of model uncertainties for a range of urban drainage models are also required to be developed. This paper, produced by the International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, is a contribution to the development of a harmonised framework for defining and assessing uncertainties in the field of urban drainage modelling. The sources of uncertainties in urban drainage models and their links are initially mapped out. This is followed by an evaluation of each source, including a discussion of its definition and an evaluation of methods that could be used to assess its overall importance. Finally, an approach for a Global Assessment of Modelling Uncertainties (GAMU) is proposed, which presents a new framework for mapping and quantifying sources of uncertainty in urban drainage models. 相似文献
14.
This paper addresses the application of a data‐based mechanistic (DBM) modelling approach using transfer function models (TFMs) with non‐linear rainfall filtering to predict runoff generation from a semi‐arid catchment (795 km2) in Tanzania. With DBM modelling, time series of rainfall and streamflow were allowed to suggest an appropriate model structure compatible with the data available. The model structures were evaluated by looking at how well the model fitted the data, and how well the parameters of the model were estimated. The results indicated that a parallel model structure is appropriate with a proportion of the runoff being routed through a fast flow pathway and the remainder through a slow flow pathway. Finally, the study employed a Generalized Likelihood Uncertainty Estimation (GLUE) methodology to evaluate the parameter sensitivity and predictive uncertainty based on the feasible parameter ranges chosen from the initial analysis of recession curves and calibration of the TFM. Results showed that parameters that control the slow flow pathway are relatively more sensitive than those that control the fast flow pathway of the hydrograph. Within the GLUE framework, it was found that multiple acceptable parameter sets give a range of predictions. This was found to be an advantage, since it allows the possibility of assessing the uncertainty in predictions as conditioned on the calibration data and then using that uncertainty as part of the decision‐making process arising from any rainfall‐runoff modelling project. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
15.
In this paper, we assess the performance of the catchment model SIMulated CATchment model (SIMCAT), to predict nitrate and soluble reactive phosphorus concentrations against four monitoring regimes with different spatial and temporal sampling frequencies. The Generalised Likelihood Uncertainty Estimation (GLUE) uncertainty framework is used, along with a general sensitivity analysis to understand relative parameter sensitivity. Improvements to model calibration are explored by introducing more detailed process representation using the Integrated Catchments model (INCA) water quality model, driven by the European hydrological predictions for the environment model. The results show how targeted sampling of headwater watercourses upstream of point discharges is essential for calibrating diffuse loads and can exert a strong influence on the whole‐catchment model performance. Further downstream, if the point discharges and loads are accurately represented, then the improvement in the catchment‐scale model performance is relatively small as more calibration points are added or frequency is increased. The higher‐order, dynamic model integrated catchments model of phosphorus dynamics, which incorporates sediment and biotic interaction, resulted in improved whole‐catchment performance over SIMCAT, although there are still large epistemic uncertainties from land‐phase export coefficients and runoff. However, the very large sampling errors in routine monitoring make it difficult to invest confidence in the modelling, especially because we know phosphorous transport to be very episodic and driven by high flow conditions for which there are few samples. The environmental modelling community seems to have been stuck in this position for some time, and whilst it is useful to use an uncertainty framework to highlight these issues, it has not widely been adopted, perhaps because there is no clear mechanism to allow uncertainties to influence investment decisions. This raises the question as to whether it might better place a cost on uncertainty and use this to drive more data collection or improved models, before making investment decisions concerning, for example, mitigation strategies. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
16.
ABSTRACTModel ensembles are possibly the most powerful tool to assess uncertainties in runoff predictions stemming from inadequacies in model structure. But in many applications little knowledge is gained about the specific weaknesses of the individual models. Here we introduce the ensemble range approach (ERA). Compared to other ensemble techniques, ERA is primarily intended to facilitate hydrological reasoning about model structural uncertainty. This is attempted by separate modelling of data uncertainty and structural uncertainty with two different error density functions that are combined in one likelihood function. The width of the structural error density is in accordance with the range of runoff predictions calculated by a small model ensemble at each individual time step. Albeit not the only choice, this study is restricted on the use of a modified beta density to represent structural uncertainty. The performance of ERA is assessed in some synthetic and real data case studies. Ensembles of two structurally identical models are applied, made possible by estimating the parameters of ERA and both models simultaneously.
Editor D. Koutsoyiannis; GUEST editor S. Weijs 相似文献
17.
Gill M. Martin 《Surveys in Geophysics》2014,35(3):553-575
Despite their obvious environmental, societal and economic importance, our understanding of the causes and magnitude of the variations in the global water cycle is still unsatisfactory. Uncertainties in hydrological predictions from the current generation of models pose a serious challenge to the reliability of forecasts and projections across time and space scales. This paper provides an overview of the current issues and challenges in modelling various aspects of the Earth’s hydrological cycle. These include: the global water budget and water conservation, the role of model resolution and parametrisation of precipitation-generating processes on the representation of the global and regional hydrological cycle, representation of clouds and microphysical processes, rainfall variability, the influence of land–atmosphere coupling on rainfall patterns and their variability, monsoon processes and teleconnections, and ocean and cryosphere modelling. We conclude that continued collaborative activity in the areas of model development across timescales, process studies and climate change studies will provide better understanding of how and why the hydrological cycle may change, and better estimation of uncertainty in model projections of changes in the global water cycle. 相似文献
18.
Assessing the uncertainties of hydrologic model selection in climate change impact studies 总被引：3，自引：0，他引：3
The uncertainties associated with atmosphere‐ocean General Circulation Models (GCMs) and hydrologic models are assessed by means of multi‐modelling and using the statistically downscaled outputs from eight GCM simulations and two emission scenarios. The statistically downscaled atmospheric forcing is used to drive four hydrologic models, three lumped and one distributed, of differing complexity: the Sacramento Soil Moisture Accounting (SAC‐SMA) model, Conceptual HYdrologic MODel (HYMOD), Thornthwaite‐Mather model (TM) and the Precipitation Runoff Modelling System (PRMS). The models are calibrated based on three objective functions to create more plausible models for the study. The hydrologic model simulations are then combined using the Bayesian Model Averaging (BMA) method according to the performance of each models in the observed period, and the total variance of the models. The study is conducted over the rainfall‐dominated Tualatin River Basin (TRB) in Oregon, USA. This study shows that the hydrologic model uncertainty is considerably smaller than GCM uncertainty, except during the dry season, suggesting that the hydrologic model selection‐combination is critical when assessing the hydrologic climate change impact. The implementation of the BMA in analysing the ensemble results is found to be useful in integrating the projected runoff estimations from different models, while enabling to assess the model structural uncertainty. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
19.
AbstractA generalized likelihood uncertainty estimation (GLUE) framework coupling with artificial neural network (ANN) models in two surrogate schemes (i.e. GAE-S1 and GAE-S2) was proposed to improve the efficiency of uncertainty assessment in flood inundation modelling. The GAE-S1 scheme was to construct an ANN to approximate the relationship between model likelihoods and uncertain parameters for facilitating sample acceptance/rejection instead of running the numerical model directly; thus, it could speed up the Monte Carlo simulation in stochastic sampling. The GAE-S2 scheme was to establish independent ANN models for water depth predictions to emulate the numerical models; it could facilitate efficient uncertainty analysis without additional model runs for locations concerned under various scenarios. The results from a study case showed that both GAE-S1 and GAE-S2 had comparable performances to GLUE in terms of estimation of posterior parameters, prediction intervals of water depth, and probabilistic inundation maps, but with reduced computational requirements. The results also revealed that GAE-S1 possessed a slightly better performance in accuracy (referencing to GLUE) than GAE-S2, but a lower flexibility in application. This study shed some light on how to apply different surrogate schemes in using numerical models for uncertainty assessment, and could help decision makers in choosing cost-effective ways of conducting flood risk analysis. 相似文献
20.
Climatization of environmental degradation: a widespread challenge to the integrity of earth science
Michael L. Wine 《水文科学杂志》2020,65(6):867-883
ABSTRACTTo guide scientists and society regarding the hydrologic consequences of anthropogenic climate change, earth scientists increasingly develop qualitative predictions and quantitative ensembles of models, some of which have important economic or (geo)political implications. However, with unprecedented human population, environmental degradation, and water scarcity, climatic factors are increasingly invoked falsely to explain failures of environmental governance, a phenomenon termed climatization. We propose a first typology of climatization in hydrology. Scientific climatization occurs when – during the normal course of a scientific investigation – a hydrologic state is falsely attributed to climatic factors, often due to a conceptual model that excludes human impacts or a simplified methodology that fails to quantify uncertainty. In contrast, securitization-aligned climatization occurs when a securitizing state actor requires that scientists attribute observed hydrologic states to climatic factors. Maintaining the credibility of earth science requires that earth scientists vigorously contest both scientific climatization and securitization of global change hydrology. 相似文献